aegis
Admission-control MCP server for AI coding agents — rejects bad file edits without coaching the LLM.
README
Aegis
Aegis ensures bad decisions don't stick.
A system for preventing silent regressions in LLM-driven workflows.
If you are an AI coding agent (Claude Code, Cursor, Aider, etc.) helping a user install or integrate Aegis, read
AGENTS.mdinstead — it has imperative install commands, integration templates you can paste, and the framing rules you must respect. The rest of this README is for the human evaluating whether to adopt Aegis.
What it is
Aegis is a constraint-based behavior harness for LLM systems. (In the broad sense: a verification environment, not an agent driver.)
For the domain-independent framework definition, see
docs/framework.md. aegis-agent is one
implementation case: Aegis applied to agent-driven code changes.
Aegis does not write code, and does not tell the LLM how to write code. It only judges whether the code an LLM produces is allowed to stay. The LLM (or whichever code generator you wrap) keeps full agency over what to write; Aegis exercises agency only over what passes its gates.
It enforces a local closed loop: each proposed state transition is validated, and regressions are rejected.
Instead of optimizing model behavior, Aegis enforces explicit, verifiable constraints, ensuring that invalid or regressive states do not persist.
Why it exists
LLM systems fail in three ways that current tooling does not catch:
- Multi-turn refactors accumulate regressions silently
- LLM-described actions diverge from actual tool calls
- Structural rules erode without anyone noticing
Aegis exists to make these failures visible and rejectable.
Core mechanism
Aegis controls state transitions:
Sₙ → Sₙ₊₁ is allowed only if all constraints are satisfied
and no regression is detected.
Otherwise, the system rolls back to Sₙ.
Cost-aware rollback is the only cross-iteration consistent criterion. Other checks (validation, structural constraints) act as local guards, not global direction signals.
What actually gets checked (V1.10, six layers)
| Layer | Checks | Where it runs |
|---|---|---|
| Ring 0 — syntax | tree-sitter parse; ERROR / MISSING node → BLOCK | aegis check, MCP, pipeline |
| Ring 0.5 — structural signals | fan_out (unique imports), max_chain_depth (longest method chain) — numeric only |
aegis check, MCP, pipeline |
| Cost regression | sum(signals_after) > sum(signals_before) → BLOCK / ROLLBACK |
MCP (when old_content given), pipeline (every iter) |
| PlanValidator | path safety / scope / dangerous_path / virtual-FS simulation | aegis pipeline run only |
| Executor + Snapshot | atomic apply with backup-dir rollback | aegis pipeline run only |
| Stalemate / Thrashing detector | sequence-level; halts the loop with a named reason | aegis pipeline run only |
aegis check and the MCP server expose the first three layers
(single-file judgement); the multi-turn pipeline adds the last
three (cross-iteration loop control).
Design principles
- Do not write code; only judge code that is written
- Do not tell the model what to write; only what cannot stay
- Only reject what is verifiably bad
- No automatic learning
- No objective optimization
The first two are the load-bearing ones. They are why Aegis can wrap any code-generating agent (Cursor, Claude Code, Aider, your own pipeline) without becoming a competing one — Aegis exercises judge agency; the wrapped agent keeps author agency.
Guarantees
- Regressions are detected and rolled back when constraints are defined
- Invalid states are blocked at the validation layer
- All decisions are recorded in a machine-readable trace
Non-goals
Aegis is not:
- An AI agent
- An optimizer
- A self-improving system
These are deliberate design choices, not future work.
Aegis will not evolve into any of these.
See docs/post_launch_discipline.md
for the explicit deferral list.
Scope
Aegis enforces correctness within a single execution loop, but does not adapt behavior across executions.
The loop is local, not global.
Quickstart
As of V1.10, Aegis is a single Rust workspace producing two
binaries — aegis (CLI) and aegis-mcp (MCP stdio server). Zero
Python at runtime.
Install
# Prerequisites: git + a Rust toolchain.
# If you don't have Rust:
# curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
# source "$HOME/.cargo/env"
git clone https://github.com/wei9072/aegis && cd aegis
cargo build --release --workspace
Install system-wide so aegis / aegis-mcp end up on $PATH:
cargo install --path crates/aegis-cli
cargo install --path crates/aegis-mcp # optional — MCP server
Cross-platform release artifacts (Linux x86_64/aarch64, macOS
x86_64/aarch64, Windows x86_64) ship via GitHub Releases — see V2.0
in docs/v1_rust_port_plan.md.
Static analysis (no LLM, no API key)
aegis check runs Ring 0 (syntax) + Ring 0.5 (structural signals
— fan-out, max chain depth) on any supported source file:
aegis languages # list supported languages
aegis check path/to/file.py # human-readable signals
aegis check path/to/file.py --json # machine-readable
The intent: drop this into a pre-commit hook or CI gate so bad
diffs never make it past the boundary you care about. See
docs/integrations/ for paste-ready examples.
LLM-backed multi-turn pipeline
aegis pipeline run drives the Planner → Validator → Executor
loop against your workspace. Provider config comes from environment
variables — supports any OpenAI-compatible endpoint (OpenAI,
OpenRouter, Groq):
export AEGIS_PROVIDER=openai # or: openrouter | groq
export AEGIS_MODEL=gpt-4o-mini # any model the provider exposes
export OPENAI_API_KEY=sk-... # or AEGIS_API_KEY (provider-agnostic)
aegis pipeline run \
--task "rename the foo helper to bar everywhere" \
--root . \
--max-iters 3
Every iteration prints a one-line summary (iter 0 [abc12345] plan=continuing patches=2 applied=true rolled_back=false); add --json for a
machine-readable summary at the end. The loop stops when the
planner declares done, signals stalemate, signals thrashing, or
hits --max-iters. Cost-aware regression rollback fires
automatically if structural signals get worse mid-loop.
MCP server (Cursor / Claude Code / your own agent)
aegis-mcp # stdio JSON-RPC, MCP protocol 2025-06-18
Configure your MCP client per
docs/integrations/mcp_design.md;
the agent then calls validate_change(path, new_content, old_content?) mid-loop and gets a {decision, reasons, signals_after, …} verdict back. Pure observation — never coaches
the agent (see Design principles).
aegis chat — interactive coding agent (V3)
Built on the same primitives, aegis chat is a substrate-mode
agent: pick any of three providers via env vars, drop into a REPL
with line editing + slash commands + markdown rendering. Three
modes auto-detected:
aegis chat "explain this concept" # one-shot
echo "task" | aegis chat # pipe → one-shot
aegis chat # tty → interactive REPL
Provider env vars (first match wins):
# OpenAI-compat (covers OpenRouter / Groq / Ollama / vLLM / etc.)
export AEGIS_OPENAI_BASE_URL=https://openrouter.ai/api/v1
export AEGIS_OPENAI_API_KEY=sk-or-v1-...
export AEGIS_OPENAI_MODEL=meta-llama/llama-3.3-70b-instruct
# Anthropic
export AEGIS_ANTHROPIC_API_KEY=sk-ant-...
export AEGIS_ANTHROPIC_MODEL=claude-haiku-4-5
# Gemini
export AEGIS_GEMINI_API_KEY=AIza...
export AEGIS_GEMINI_MODEL=gemini-2.5-flash
Common flags:
aegis chat --tools --workspace . # add Read/Glob/Grep tools
aegis chat --tools --mcp aegis-mcp # mount aegis-mcp as a tool
aegis chat --verify # auto-detect test runner
aegis chat --cost-budget 5.0 # terminate on cumulative regression
aegis chat --permission-mode read-only # safest sandbox
The four V3 differentiation points (PreToolUse aegis-predict,
cross-turn cost tracking, verifier-driven done, stalemate detection)
are wired in; full usage walkthrough in
docs/v3_dogfood.md, design rationale in
docs/v3_agent_design.md.
Integrations
You're already using Cursor / Claude Code / Aider / Copilot / your own agent. Aegis is meant to be a side-channel enforcement layer that doesn't ask you to switch tools.
| Boundary | Path | Status |
|---|---|---|
| Commit | Git pre-commit hook | ✓ ready (5-line bash) |
| PR / merge | GitHub Action / CI gate | ✓ ready (10-line YAML) |
| Agent decision | MCP server | ✅ validate_change ready (cargo install --path crates/aegis-mcp && aegis-mcp) |
Pick whichever boundary fits your workflow; you can stack them.
Index + per-path detail: docs/integrations/.
Status
| Layer | State | Notes |
|---|---|---|
| Execution Engine | ✅ | Pipeline + Executor + cost-aware regression rollback. Native Rust loop in aegis-runtime::native_pipeline. |
| Static analysis | ✅ | Ring 0 (syntax) + Ring 0.5 (fan_out, max_chain_depth) shared by aegis check + aegis pipeline run + aegis-mcp validate_change. |
| Decision Trace | ✅ | DecisionTrace + 10-value DecisionPattern + 5-value TaskVerdict; Python-era cross-model evidence in docs/v1_validation.md. Rust re-validation is gated on LLM API budget (V1.8). |
| MCP server | ✅ | aegis-mcp — hand-rolled JSON-RPC 2.0 over stdio; one tool: validate_change per docs/integrations/mcp_design.md. |
| Cross-model sweep harness | 🟡 | aegis pipeline run works scenario-by-scenario; batch sweep (aegis sweep) is V1.8 backlog — gated on API budget. |
| Feedback Layer | ❌ | Out of scope by design — see Non-goals and Critical Principle. Structurally enforced by crates/aegis-decision/tests/contract.rs. |
Supported source languages (Ring 0 + Ring 0.5 signals)
Tier 2 multi-language support landed in V1.4–V1.7 of the Rust port
(see docs/v1_rust_port_plan.md). With
V1.10's Python deletion, every language listed below now gets both
enforcement (Ring 0 + Ring 0.5 via aegis check) and the
refactor half (aegis pipeline run against an OpenAI-compatible
LLM provider). Run aegis languages for the live registry.
| Language | Ring 0 syntax | Ring 0.5 fan-out | Ring 0.5 chain-depth | Extensions |
|---|---|---|---|---|
| Python | ✅ | ✅ | ✅ | .py, .pyi |
| TypeScript | ✅ | ✅ | ✅ | .ts, .tsx, .mts, .cts |
| JavaScript | ✅ | ✅ | ✅ | .js, .mjs, .cjs, .jsx |
| Go | ✅ | ✅ | ✅ | .go |
| Java | ✅ | ✅ | 🟡 | .java |
| C# | ✅ | ✅ | ✅ | .cs |
| PHP | ✅ | ✅ | ✅ | .php, .phtml, .php5, .php7, .phps |
| Swift | ✅ | ✅ | ✅ | .swift |
| Kotlin | ✅ | ✅ | ✅ | .kt, .kts |
| Dart | ✅ | ✅ | 🟡 | .dart |
| Rust | ✅ | ✅ | ✅ | .rs |
🟡 = the default chain-depth walker under-counts on this language's
AST shape; per-language overrides are the planned fix path
(LanguageAdapter::max_chain_depth).
Adding a language is one Cargo dep + one adapter file under
crates/aegis-core/src/ast/languages/ + one .scm query —
checklist in docs/multi_language_plan.md#per-language-work-checklist.
Philosophy
If Aegis starts learning automatically, it has violated its own design.
License
MIT — see LICENSE.
V1.10 — Rust workspace, zero Python at runtime. Cross-platform
release artifacts (Homebrew, npm, GitHub Releases) are templated
under packaging/; activation is the V2.0 milestone
in docs/v1_rust_port_plan.md.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。